Four competing models are used to predict the point value of the current and ensuing possessions using the predictor variables in Table REF . The first model is a linear regression model with no interactions. The second is a linear regression that includes all \(2^6 = 64\)  interactions between the nine predictors, with the exception of any interactions involving offensive pace, defensive pace, or number of possessions. The third and fourth models are a random forest model and an extreme gradient boosting (XGBoost) model, respectively. Literature on linear regression is prevalent, and the technique is commonly applied. However, random forests and the XGBoost models are more current, lesser known techniques. Random forest and XGBoost models have become more important with advances in computation. Sections REF  and REF  provide overviews of these techniques. For more detail on random forest see [1]}, and for more detail on XGBoost, see [2]} and [3]}.
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